Machine learning mortality prediction model for cyclosporine therapy in pediatric aplastic anemia.

ABSTRACT: The outcomes of children with aplastic anemia receiving cyclosporine monotherapy vary significantly in terms of mortality risk; therefore, a prognostic model for predicting mortality risk was constructed to optimize risk-stratified treatment strategies. This retrospective cohort study included children with acquired AA receiving cyclosporine-based immunosuppression, stratified by disease severity (vSAA/SAA/NSAA) and randomly split into training (70%) and validation (30%) cohorts. Ten machine learning models were developed; hyperparameters were optimized via grid search with 10-fold cross-validation exclusively within the training cohort to prevent data leakage. Model performance was evaluated using area under the ROC curve (AUC), accuracy, recall, specificity, precision, F1 score, and Brie

SUBMITTER: Wen X 

PROVIDER: S-EPMC12858566 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature